convert code written in python to be compatible with jq command

I have this python code that works.
But I will like to use jq command line utility so that it can be part of shell script.

import pandas as pd
import json

with open('output.txt') as f:
    x = json.load(f)
	
df = pd.DataFrame(x['Datapoints'])

df.sort_values('Timestamp')

Update:

I am getting correct output as shown below:

$ cat output.txt  | jq ' .Datapoints[] | {cidr: .Timestamp, comment: .Average} | join(",")' | more

"2023-06-12T08:19:00+00:00,0.18333333333333332"
"2023-06-12T04:10:00+00:00,0.03333333333333333"
"2023-06-12T07:42:00+00:00,0.03333333333333333"
"2023-06-12T03:33:00+00:00,0"

But I do not need double quotes and second field should be 0.18 instead of 0.1833

Update 2:

# head -20 output.txt
{
    "Label": "DBLoad",
    "Datapoints": [
        {
            "Timestamp": "2023-06-12T08:19:00+00:00",
            "Average": 0.18333333333333332,
            "Unit": "None"
        },
        {
            "Timestamp": "2023-06-12T04:10:00+00:00",
            "Average": 0.03333333333333333,
            "Unit": "None"
        },
        {
            "Timestamp": "2023-06-12T07:42:00+00:00",
            "Average": 0.03333333333333333,
            "Unit": "None"
        },
        {
            "Timestamp": "2023-06-12T03:33:00+00:00",

You want to use the @csv filter with -r/--raw-output on a sorted array (and probably apply rounding to numbers, if I read your question right)

jq -r '.Datapoints
| sort_by(.Timestamp)[]
| [.Timestamp, (.Average*100|round/100)]
| @csv' output.txt

Example output:

"2023-06-12T04:10:00+00:00",0.03
"2023-06-12T07:42:00+00:00",0.03
"2023-06-12T08:19:00+00:00",0.18